Published in:
Bioinformatics, 29 (2013) 16, Seite 2004-2008
Language:
English
DOI:
10.1093/bioinformatics/btt307
ISSN:
1367-4811;
1367-4803
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title>
<jats:p>Motivation: The identification of drug–target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug–target domain.</jats:p>
<jats:p>Results: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.</jats:p>
<jats:p>Availability: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.</jats:p>
<jats:p>Contact: apulvirenti@dmi.unict.it</jats:p>
<jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p>